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Record W2475388485 · doi:10.1017/cbo9780511977046.002

Understanding Institutional Economics

2011· book-chapter· en· W2475388485 on OpenAlexaff
Malcolm Rutherford

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEconomicsPolitical science

Abstract

fetched live from OpenAlex

If institutionalism today is associated with a single figure, it is with Thorstein Veblen, but the terms “institutional economics” or “institutionalism” to denote an identifiable movement within American economics did not come into common use until the interwar period. Veblen's most substantial and influential writing appeared in the years between 1898 and 1914, and although Veblen was far from uninvolved, it was those influenced by Veblen's ideas, such as Walton Hamilton, Wesley Mitchell, and J. M. Clark, rather than Veblen himself, who played the major role in defining and promoting what became known as institutional economics (Rutherford 2000a, 2000b). “INSTITUTIONAL ECONOMICS” The very first mention of the term “institutional economics” in print occurs in a footnote in Walton Hamilton's 1916 article dealing with the work of Robert Hoxie (Hamilton 1916a, p. 863, n.5). Hoxie and Hamilton were colleagues for a short time at Chicago, and Hamilton claims that Hoxie called himself an “institutional economist.” Hoxie had been a student of Veblen's at Chicago and was initially much influenced by Veblen's ideas on the effect of the “industrial discipline” on the habits of thought of unionized workers. Hoxie was also a highly regarded investigator of the American labor movement, an influential teacher, and a point of connection between those around Veblen and other students of the labor movement, particularly John R. Commons and some of his students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.009
Scholarly communication0.0060.007
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.142
GPT teacher head0.184
Teacher spread0.042 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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